Deriving Long-Term Snow Depth Datasets From Passive Microwave Observations-A Case Study In The United States

This study investigated a data fusion method based on pixel-based robust stepwise regression technique to retrieve a long-term snow depth dataset from passive microwave observations. The NOAA's Snow Data Assimilation System (SNODAS) snow depth(SD) product covered the United States was selected as standard reference to train the brightness temperature data from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature Earth System Data Record. In order to achieve robustness against the presence of outliers and avoid multicollinearity problem in regression, the robust stepwise regression technique was selected as the training approach. The retrieved snow depth were evaluated against in situ observations and SNODAS SD. The results show that the retrieved SD have a good agreement with both in situ observations and SNODAS SD, and are more consistent with SNODAS SD than in situ data.